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  1. Proceedings of the 2011 ACM SIGPLAN Workshop on Memory Systems Performance and Correctness (MSPC '11)
  2. How to fit program footprint curves
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There is nothing wrong with out-of-thin-air: compiler optimization and memory models
Extended sequential reasoning for data-race-free programs
The impact of diverse memory architectures on multicore consumer software: an industrial perspective from the video games domain
Garbage collection for multicore NUMA machines
A programming model for deterministic task parallelism
Data-race exceptions have benefits beyond the memory model
Let there be light!: the future of memory systems is photonics and 3D stacking
Deferred gratification: engineering for high performance garbage collection from the get go
Performance implications of fence-based memory models
Minor memory references matter in collaborative caching
Approximating inclusion-based points-to analysis
How to fit program footprint curves

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How to fit program footprint curves

Content Provider ACM Digital Library
Author Bao, Bin Xiang, Xiaoya
Abstract A footprint is the volume of data accessed in a time window. A complete characterization requires summarizing all footprints in all execution windows. A concise summary is the footprint curve, which gives the average footprint in windows of different lengths. The footprint curve contains information from all footprints. It can be measured in time O(n) for a trace of length n, which is fast enough for most benchmarks. In this paper, we outline a study on footprint curves. We propose four curve fitting methods based on the real data observed in SPEC benchmark programs.
Starting Page 20
Ending Page 21
Page Count 2
File Format PDF
ISBN 9781450307949
DOI 10.1145/1988915.1988920
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-06-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Data footprint Cross-input prediction Curve fitting
Content Type Text
Resource Type Article
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